Image processing apparatus, image processing method, and storage medium

ABSTRACT

An image processing apparatus configured to extract an irradiation field from an image obtained through radiation imaging, comprises: an inference unit configured to obtain an irradiation field candidate in the image based on inference processing; a contour extracting unit configured to extract a contour of the irradiation field based on contour extraction processing performed on the irradiation field candidate; and a field extracting unit configured to extract the irradiation field based on the contour.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to an image processing apparatus, an imageprocessing method, and a storage medium for extracting an irradiationfield of a radiograph.

Description of the Related Art

In recent years, radiation imaging apparatuses have been widely used inmedical settings, and radiographs are obtained as digital signals,subjected to image processing, and then displayed by a display apparatusand used to make a diagnosis.

In radiation imaging, irradiation of a region other than a field ofinterest (hereinafter referred to as an “irradiation field”) that isnecessary for diagnosis is usually prevented by narrowing down theirradiation field using a collimator in order to suppress the influenceof radiation on a region other than the irradiation field, preventscattering from the outside of the irradiation field, and prevent areduction in contrast.

With regard to images obtained by narrowing down the irradiation field,in order to perform image processing on the irradiation field that isthe field of interest in diagnosis, various types of technology havebeen proposed for extracting the irradiation field.

For example, Japanese Patent Laid-Open No. 2015-123157 proposestechnology for extracting an irradiation field by obtaining a pluralityof contours based on the edge intensity in an image and performingcorrectness determination. Also, Japanese Patent Laid-Open No. 04-261649proposes technology for inputting image data to a neural network andoutputting an irradiation field as a result.

However, a human body, which is the subject, may include a structuresuch as a bone, an implant, or the like that has a strong edge componentand is difficult to distinguish from contours used for narrowing downthe irradiation field, and there may be a case where the irradiationfield cannot be recognized using the technology disclosed in JapanesePatent Laid-Open No. 2015-123157.

The technology disclosed in Japanese Patent Laid-Open No. 04-261649enables determination of the irradiation field based on comprehensivefeatures of a larger number of images using a neural network, but theremay be a case where it is difficult to classify a region as theirradiation field using the neural network only.

The present invention was made in view of the above problems andprovides image processing technology that enables extraction of anirradiation field.

SUMMARY OF THE INVENTION

According to one aspect of the present invention, there is provided animage processing apparatus configured to extract an irradiation fieldfrom an image obtained through radiation imaging, comprising: aninference unit configured to obtain an irradiation field candidate inthe image based on inference processing; a contour extracting unitconfigured to extract a contour of the irradiation field based oncontour extraction processing performed on the irradiation fieldcandidate; and a field extracting unit configured to extract theirradiation field based on the contour.

According to another aspect of the present invention, there is providedan image processing method for extracting an irradiation field from animage obtained through radiation imaging, comprising: obtaining anirradiation field candidate in the image based on inference processing;extracting a contour of the irradiation field based on contourextraction processing performed on the irradiation field candidate; andextracting the irradiation field based on the contour.

According to the present invention, image processing technology thatenables extraction of an irradiation field can be provided.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments (with reference to theattached drawings).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram showing an exemplary basic configuration of aradiation imaging system that includes an image processing apparatusaccording to an embodiment.

FIG. 1B is a block diagram illustrating an exemplary basic configurationof an irradiation field recognizing unit.

FIG. 2A is a flowchart illustrating the flow of processing performed bythe irradiation field recognizing unit.

FIG. 2B is a schematic diagram showing images that are processed inprocessing performed by the irradiation field recognizing unit.

FIG. 3A is an illustrative diagram showing the concept of learning by aneural network.

FIG. 3B is an illustrative diagram showing the concept of inference bythe neural network.

FIG. 4A is a flowchart illustrating the flow of contour extractionprocessing.

FIG. 4B is a schematic diagram showing an image that is processed in thecontour extraction processing.

FIG. 4C is a diagram showing an example of transformation of an imageinto a polar coordinate space.

FIG. 5A is a flowchart illustrating the flow of contour extractionprocessing.

FIG. 5B is a schematic diagram showing images that are processed in thecontour extraction processing.

FIG. 6 is a block diagram showing an exemplary basic configuration of aradiation imaging system that includes an image processing apparatusaccording to an embodiment.

DESCRIPTION OF THE EMBODIMENTS

The following describes exemplary embodiments of the present inventionin detail with reference to the drawings. It should be noted thatradiation in the present invention includes not only commonly usedX-rays but also α-rays, β-rays, γ-rays, and the like that are beamsconstituted by particles (including photons) that are emitted throughradioactive decay, as well as beams (for example, particle beams andcosmic rays) that have substantially equivalent or higher levels ofenergy. The following describes an exemplary case where X-rays are usedas radiation.

First Embodiment: Rectangular Collimator

First, an exemplary configuration of an image processing apparatusaccording to a first embodiment of the present invention will bedescribed with reference to FIGS. 1A and 1B. FIG. 1A is a block diagramshowing an exemplary basic configuration of a radiation imaging systemthat includes the image processing apparatus of the first embodiment.

A radiation imaging system 100 includes a radiation generating apparatus101 that generates radiation, a bed 103 on which a subject 102 isarranged, a detection apparatus 104 (FPD) that detects radiation andoutputs image data corresponding to radiation that has passed throughthe subject 102, a control apparatus 105 that controls radiationgeneration timing and radiation generation conditions of the radiationgenerating apparatus 101, a data collecting apparatus 106 that collectsvarious types of digital data, and an information processing apparatus107 that performs image processing and controls the entire apparatusaccording to instructions from a user. It should be noted that theconfiguration of the radiation imaging system 100 may also be referredto as a radiation imaging apparatus.

The information processing apparatus 107 includes an image processingapparatus 108 that includes an irradiation field recognizing unit 109and a diagnosis image processing unit 110, a CPU 112, a memory 113, anoperation panel 114, a storage apparatus 115, and a display apparatus116, and these are electrically connected to each other via a CPU bus111.

Various types of data and the like that are necessary for processingperformed in the CPU 112 are stored in the memory 113, and the memory113 includes a working memory that is used by the CPU 112. The CPU 112is configured to control operations of the entire apparatus using thememory 113 according to instructions input by a user through theoperation panel 114.

FIG. 1B is a block diagram showing an example of a basic functionalconfiguration of the irradiation field recognizing unit 109 in the imageprocessing apparatus of the first embodiment. The irradiation fieldrecognizing unit 109 includes, as functional units, a preprocessing unit120, an inference unit 121, a contour extracting unit 122, and a fieldextracting unit 123.

The radiation imaging system 100 starts an imaging sequence of thesubject 102 according to an instruction given by a user through theoperation panel 114. Radiation according to predetermined conditions isgenerated by the radiation generating apparatus 101, and the detectionapparatus 104 is irradiated with radiation that has passed through thesubject 102. Here, the control apparatus 105 controls the radiationgenerating apparatus 101 based on radiation generation conditions suchas the voltage, current, irradiation period, and the like, and causesthe radiation generating apparatus 101 to generate radiation underpredetermined conditions.

The detection apparatus 104 detects radiation that has passed throughthe subject 102, converts the detected radiation into an electricalsignal, and outputs the signal as image data that corresponds to theradiation. The image data output from the detection apparatus 104 iscollected as digital image data by the data collecting apparatus 106.The data collecting apparatus 106 transfers the image data collectedfrom the detection apparatus 104 to the information processing apparatus107. In the information processing apparatus 107, the image data istransferred via the CPU bus 111 to the memory 113 under controlperformed by the CPU 112.

The image processing apparatus 108 applies various types of imageprocessing to the image data stored in the memory 113 to extract anirradiation field from the image obtained through radiation imaging.After a subject region is extracted by the irradiation field recognizingunit 109, the diagnosis image processing unit 110 of the imageprocessing apparatus 108 applies diagnosis image processing such asgradation processing, enhancement processing, or noise reductionprocessing to create an image that is suitable for diagnosis. The imageprocessing apparatus 108 stores the result of processing performed bythe diagnosis image processing unit 110 in the storage apparatus 115 anddisplays the result on the display apparatus 116.

Next, processing that is performed by the irradiation field recognizingunit 109 will be described with reference to FIGS. 2A and 2B. FIG. 2A isa flowchart showing the flow of processing that is performed by theirradiation field recognizing unit 109, and FIG. 2B is a schematicdiagram showing examples of images that are processed in the processingperformed by the irradiation field recognizing unit 109. The followingdescribes an example in which a hand of the subject 102 is imaged usinga rectangular collimator. However, the present invention is not limitedby the imaged portion and the shape of the collimator described in thefirst embodiment, and can also be applied to other portions of thesubject 102 such as the chest or abdomen, or any shape for narrowingdown the irradiation field, such as a case where a circular collimatoris used, for example.

In step S201, the preprocessing unit 120 performs preprocessing of aninput image. Through this preprocessing, the input image is convertedinto a format with which subsequent inference processing that isperformed using a neural network can be effectively performed. Here, thepreprocessing unit 120 can convert the input image into an image of thesame type as images that are used for learning by the inference unit121, for example, learning by a neural network. As one example of thepreprocessing performed by the preprocessing unit 120, the preprocessingunit 120 can perform grid removal processing, scattered ray reductionprocessing, noise reduction processing, logarithmic conversionprocessing, and normalization processing, and thereafter performprocessing for scaling up or down to an image size that is suitable fora neural network that is used in the inference processing performed bythe inference unit 121.

Although any image size can be employed, if the image input to thepreprocessing unit 120 has an aspect ratio of 1:1 in length and width,such as 112×112 pixels or 224×224 pixels, for example, capability ofgeneralization to rotation can be enhanced. Also, as one example of thenormalization processing, the preprocessing unit 120 can normalize thesignal level such that the signal of the input image is changed from 0to 1. The preprocessing unit 120 obtains a preprocessed image 211 byexecuting the above processing. As shown in FIG. 2B, the preprocessedimage 211 includes an irradiation field 212 and a collimator region 213at a given ratio.

In step S202, the inference unit 121 obtains, based on inferenceprocessing, an irradiation field candidate in the image obtained throughradiation imaging. The inference unit 121 performs the inferenceprocessing (for example, inference processing using a neural network) onthe preprocessed image 211, which has been preprocessed, to obtain theirradiation field candidate from the image obtained through radiationimaging. The inference unit 121 performs the inference processing basedon learning that is performed using a set of data that includes imagesobtained through radiation imaging as input and irradiation fields asoutput. The inference unit 121 performs the inference processing basedon a result of learning that includes learning by a neural network. Forexample, the inference unit 121 can use a neural network that hasperformed learning in advance, as the neural network used in theinference processing. Details of learning by the neural network and theinference processing using the neural network will be described later.It should be noted that the inference processing is not limited toprocessing performed using a neural network, and the inference unit 121can also use a processing unit that is created through machine learningperformed using a support-vector machine or boosting, for example.

The inference unit 121 obtains, as the irradiation field candidate, aprobability map 214 that indicates the probability of “being theirradiation field” or “not being the irradiation field (i.e., being thecollimator region)” for each pixel of the input image. Here, theinference processing performed using a neural network uses a largeamount of features that cannot be obtained by a person and enablesdetermination of the irradiation field using comprehensive features ofan image that are not limited to edge features, but it is sometimesdifficult to classify a region as the irradiation field through thisprocessing alone. For example, in the probability map 214 shown in FIG.2B, a region 215 that is highly likely to be the irradiation field and aregion 216 that is highly likely to be the collimator are obtained, butthere may be a case where the probability map includes a misdetectionregion 217 that is actually not the collimator region but is determinedas being the collimator region.

In step S203, the contour extracting unit 122 extracts contours of theirradiation field based on the irradiation field candidate. The contourextracting unit 122 extracts the contours of the irradiation field basedon contour extraction processing performed on the irradiation fieldcandidate. The contour extracting unit 122 performs the contourextraction processing on the probability map 214 (irradiation fieldcandidate) obtained in step S202 by extracting contours 218 based on theshape of the collimator.

The contour extracting unit 122 performs the contour extractionprocessing on the irradiation field candidate by extracting contoursbased on the shape of the collimator. The contour extracting unit 122changes the contour extraction processing according to the shape of thecollimator. For example, the contour extracting unit 122 is capable ofperforming rectangular contour extraction processing when the shape ofthe collimator is rectangular and performing circular contour extractionprocessing when the shape of the collimator is circular. The contourextracting unit 122 is capable of selecting contour extractionprocessing for a rectangular shape of the collimator and contourextraction processing for a circular shape of the collimator.

In the first embodiment, it is assumed that the collimator (rectangularcollimator) has a rectangular contour, and the contour extracting unit122 extracts straight lines as contour candidates by performingrectangular contour extraction processing. That is, the contourextracting unit 122 performs contour extraction processing forobtaining, from the probability map 214, at most four straight lines ascontour lines that constitute the contours of the collimator based onthe rectangular shape of the collimator. Then, final contours are setbased on validity confirmation processing that is performed on theextracted contour candidates. Details of this contour extractionprocessing will be described later.

In step S204, the field extracting unit 123 extracts the irradiationfield based on the contours 218. Specifically, the field extracting unit123 performs irradiation field extraction processing to obtain a dividedregion image 219 that is an image obtained by dividing the input imageinto an irradiation field 221 and a collimator region 220. Here, thefield extracting unit 123 extracts a region inside the contours 218 asthe irradiation field 221. The region inside the contours 218 is aregion that is surrounded by the contours 218. That is, the fieldextracting unit 123 extracts a region surrounded by the contours 218 asthe irradiation field 221 based on information regarding the contours218 extracted in step S203, and extracts a region other than this region(i.e., region that is not surrounded by the contours 218) as thecollimator region 220. Through the irradiation field extractionprocessing performed by the field extracting unit 123, the irradiationfield 221 can be extracted with the misdetection region 217 included inthe result of the inference processing performed using the neuralnetwork being eliminated.

It should be noted that the field extracting unit 123 can perform theirradiation field extraction processing by extracting the irradiationfield based on a region in which an irradiation field that is presumedfrom the contours 218 overlaps with an irradiation field that ispresumed from the probability map 214. That is, the field extractingunit 123 can extract, as the irradiation field, a region in which theratio of overlap between the irradiation field presumed from thecontours 218 and the irradiation field presumed from the probability map214 is at least a set value. For example, the field extracting unit 123can perform the irradiation field extraction processing based oninformation regarding the contours 218 (S203) and information regardingthe probability map 214 (S202) by extracting, as the irradiation field221, a region in which the ratio of overlap between a region that can beformed by the contours 218 and the region 215 that is highly likely tobe the irradiation field in the probability map 214 is at least apredetermined reference value (for example, 75%).

Through the processing performed in above steps S201 to S204, theirradiation field recognizing unit 109 can extract the irradiation fieldwith high accuracy.

Next, details of processing performed by the inference unit 121 will bedescribed with reference to FIGS. 3A and 3B. FIG. 3A is an illustrativediagram showing the concept of learning by a neural network and FIG. 3Bis an illustrative diagram showing the concept of inference by theneural network.

Learning by the neural network is performed using a set of input data301 and a corresponding set of training data 305.

First, inference processing is performed on the set of input data 301using a neural network 302 that is in the process of learning, to outputa set of inference results 304. Next, a loss function is calculated fromthe set of inference results 304 and the set of training data 305. Anyfunction such as a square error function or a cross entropy errorfunction can be used as the loss function. Further, back propagationstarting from the loss function is performed to update a parameter groupof the neural network 302 in the process of learning. Learning by theneural network 302 in the process of learning can be advanced byrepeating the above processing while changing the set of input data 301and the set of training data 305.

Inference by the neural network is processing for applying inferenceprocessing that is performed using a learned neural network 307 to inputdata 306 and outputting an inference result 308.

In the first embodiment, the set of training data 305 is set such that,in the set of input data 301, the irradiation field is represented by 1and the collimator region is represented by 0. The set of training data305 may be created as appropriate through manual input or may beobtained by the irradiation field recognizing unit 109 from an outsidesource. Alternatively, the irradiation field recognizing unit 109 mayautomatically create standard training data according to the imagedportion.

Here, the neural network 302 has a structure in which a large number ofprocessing units 303 are randomly connected to each other. Examples ofthe processing units 303 perform processing including convolutioncalculation, normalization processing such as batch normalization, andprocessing performed using an activation function such as ReLU orSigmoid, and the processing units have parameter groups for describingcontent of processing. For example, a set of these processing units thatperform processing in the order of, for example, convolutioncalculation, normalization, and an activation function are arranged inthree to about a few hundred layers and are connected to each other, andcan form a structure called a convolutional neural network or arecurrent neural network.

It should be noted that input and output of the neural network 302 takethe form of an image that has been processed by the preprocessing unit120. It is possible to employ a configuration in which an output imagehas the same resolution as an input image and indicates the probabilityof being the irradiation field for each pixel. In another example, theresolution of the output image may be set lower than that of the inputimage. In this case, the processing period of learning and inference canbe reduced, but accuracy of the entire processing including subsequentprocessing may be reduced as a tradeoff.

Next, details of the contour extraction processing performed by thecontour extracting unit 122 will be described with reference to FIGS.4A, 4B, and 4C. FIG. 4A is a flowchart showing the flow of the contourextraction processing, FIG. 4B is a schematic diagram showing an imagethat is processed in the contour extraction processing, and FIG. 4C is adiagram showing an example of transformation of an image into a polarcoordinate space. The contour extracting unit 122 extracts contours ofthe irradiation field from an image that is obtained from theprobability map 214 and includes edges that indicate boundaries betweenthe irradiation field and the collimator region.

In step S401, the contour extracting unit 122 extracts, from theprobability map 214, edges that indicate boundaries between the region215 that is highly likely to be the irradiation field and the region 216that is highly likely to be the collimator. Although the method forextracting edges is not limited, edges can be extracted by applying adifferential filter such as a sobel filter to the probability map 214,for example. Although a probability from 0 to 1 is set for each pixel ofthe probability map 214, it is also possible to perform binary codedprocessing based on a preset threshold value (for example, 0.5) andperform processing for extracting pixels having at least the thresholdvalue in order to simplify edge extraction processing. Through the aboveprocessing, an image 411 including edges 412 that include boundariesbetween the irradiation field and the collimator region can be obtained.

In step S402, the contour extracting unit 122 applies the Houghtransform to the image 411. Here, a point on the image 411 that isrepresented as (x, y) in a rectangular coordinate system is transformedinto a polar coordinate space of angle θ and distance ρ using Formula 1.Here, θ is the angle between the x axis and a line that is drawn fromthe origin perpendicularly to a straight line that passes through (x, y)and ρ is the length of the line drawn from the origin perpendicularly tothe straight line passing through (x, y). When transformation isperformed in a range of −90°<θ≤90°, for example, distribution in thepolar coordinate space is obtained as shown in FIG. 4C. Here, a pair (θ,ρ) that takes a local maximum value in the polar coordinate spaceindicates a high probability of existence of a straight line in theimage in the rectangular coordinate system. Using this feature, contoursof the rectangular collimator that has a linear structure can be easilyextracted through application of the Hough transform.

ρ=x cos θ+y sin θ  Formula 1

In step S403, the contour extracting unit 122 extracts the longeststraight line 413 as a contour candidate from the image 411. In thisstep, the contour extracting unit 122 searches the entire polarcoordinate space and extracts a straight line that is formed by a pair(θ, ρ) 417 that takes the maximum value in the polar coordinate space.

In step S404, the contour extracting unit 122 extracts a straight line414 that is opposite and parallel to the straight line 413 as a contourcandidate. Assuming that the collimator has a rectangular shape, it isthought that there is one side that extends in a direction parallel toanother side. Based on this assumption, the contour extracting unit 122searches the polar coordinate space for a local maximum value in aregion 421 where θ is in a predetermined range with respect to the pair(θ, ρ) 417 that corresponds to the straight line 413. The range of θ canbe set to about 5° to 15° relative to θ=−90° or about −(5° to 15°)relative to θ=90°. Thus, the contour extracting unit 122 can extract apair (θ, ρ) 418 as a local maximum value other than the pair (θ, ρ) 417and a straight line 414 corresponding to the pair 418.

In step S405, the contour extracting unit 122 extracts a straight line415 that crosses and is perpendicular to the straight line 413 as acontour candidate. Assuming that the collimator has a rectangular shape,it is thought that there is one side that extends in a directionperpendicular to another side. Based on this assumption, the contourextracting unit 122 searches the polar coordinate space for a pair (θ,ρ) in a region 422 where θ is in a predetermined range in the polarcoordinate space with respect to the pair (θ, ρ) 417 corresponding tothe straight line 413. The search range can be set to any range that isabout ±15° relative to θ=0° that has a phase difference of +90° from θ(=90°) of the reference pair 417. Thus, the contour extracting unit 122can extract a pair (θ, ρ) 419 as a point at which a waveform 431 passingthrough the pair (θ, ρ) 417 and a waveform 432 passing through the pair(θ, ρ) 418 cross each other, and a straight line 415 corresponding tothe pair 419.

In step S406, the contour extracting unit 122 extracts a straight line416 that is opposite and parallel to the straight line 415 as a contourcandidate. Similarly to step S404, the contour extracting unit 122searches for a pair (θ, ρ) from a region 423 in the polar coordinatespace to search for a side that extends in a direction parallel to thestraight line 415. The region 423 used as the search range can be setnarrower than the region 422 from which the pair (θ, ρ) 419 has beenextracted. The contour extracting unit 122 extracts a pair (θ, ρ) 420 atwhich a waveform 433 passing through the pair (θ, ρ) 417 and a waveform434 passing through the pair (θ, ρ) 418 cross each other, and a straightline 416 corresponding to the pair 420 from the region 423. It should benoted that, if a straight line is not found in any of steps S403 toS406, processing in that step can be skipped supposing that there is nostraight line.

In step S407, the contour extracting unit 122 confirms whether thestraight lines 413 to 416 that are contour candidates extracted in stepsS403 to S406 are valid as contours of the irradiation field and thecollimator region. For example, the contour extracting unit 122 candetermine whether the extracted straight lines are longer than apredetermined length. Based on this determination, the contourextracting unit 122 extracts, out of the straight lines extracted ascontour candidates, straight lines that are longer than thepredetermined length as contours.

Alternatively, the contour extracting unit 122 can determine whether aregion that is formed by the extracted straight lines overlaps andmatches well with the region 215 that is highly likely to be theirradiation field in the probability map 214, for example, whether anoverlap ratio that indicates the ratio of overlap between these regionsis at least a threshold value. If the overlap ratio that indicates theratio of overlap between the region based on the contour candidates(region based on the extracted straight lines 413 to 416) and anirradiation field presumed from the probability map 214 is at least athreshold value, the contour extracting unit 122 extracts the contourcandidates (straight lines 413 to 416) as contours.

With regard to confirmation of validity of the contours, the contourextracting unit 122 can perform determination processing that matchesfeatures of the image obtained through radiation imaging, such as theimaged portion of the subject. If validity of a straight line is notconfirmed in this step, the contour extracting unit 122 eliminates thestraight line, performs another search as necessary, and outputs a groupof remaining straight lines as final contours. Through the processingperformed in above steps S401 to S407, the contour extracting unit 122can extract contours with high accuracy.

According to the first embodiment, image processing technology can beprovided with which an irradiation field can be accurately extractedeven if the irradiation field includes a structure that has a strongedge component and is difficult to distinguish from contours used fornarrowing down the irradiation field.

Second Embodiment: Circular Irradiation Field

Next, a second embodiment of the present invention will be described. Inthe second embodiment, an exemplary configuration of a case where acircular collimator is used for narrowing down the irradiation fieldwill be described. Examples of configurations of the radiation imagingsystem 100 and the image processing apparatus are similar to those inthe first embodiment. The second embodiment differs from the firstembodiment in that the contour extracting unit 122 performs circularcontour extraction processing, assuming the use of a circularcollimator. In the second embodiment, the contour extracting unit 122extracts a circle or an ellipse as a contour candidate by performing thecircular contour extraction processing.

Details of the contour extraction processing performed by the contourextracting unit 122 will be described with reference to FIGS. 5A and 5B.FIG. 5A is a flowchart showing the flow of the contour extractionprocessing in the second embodiment and FIG. 5B is a schematic diagramshowing images that are processed in the contour extraction processingin the second embodiment.

Assume that an image 511 that is obtained by narrowing down theirradiation field using a circular collimator is input to theirradiation field recognizing unit 109 and a probability map 512 isoutput by the inference unit 121. In the probability map 512, a region515 that is highly likely to be the irradiation field and a region 516that is highly likely to be the collimator are obtained, but there maybe a case where the probability map includes a misdetection region 517that is actually not the collimator region but is determined as beingthe collimator region.

In step S501, the contour extracting unit 122 obtains, from theprobability map 512, an image 513 that includes edges that indicateboundaries between the region 515 that is highly likely to be theirradiation field and the region 516 that is highly likely to be thecollimator. This processing is equivalent to step S401 in FIG. 4A.

In step S502, the contour extracting unit 122 applies the Houghtransform to the image 513. Here, a point on the image 513 that isrepresented as (x, y) in a rectangular coordinate system is transformedinto a three-dimensional Hough space of the center point (center X,center Y) and the radius r of a circle using Formula 2.

r ²=(x−centerX)²+(y−centerY)²  Formula 2

Alternatively, assuming that the collimator has an elliptical contour,the contour extracting unit 122 can transform a point on the image 513that is represented as (x, y) in a rectangular coordinate system into afour-dimensional Hough space of the center point (center X, center Y) ofan ellipse and the major axis a and the minor axis b of the ellipseusing Formula 3.

${\frac{\left( {x - {centerX}} \right)^{2}}{a^{2}} + \frac{\left( {y - {centerY}} \right)^{2}}{b^{2}}} = 1$

In step S503, the contour extracting unit 122 extracts a circularcontour 514 by selecting coordinates in the Hough space that correspondto the circular contour 514 from the result of the Hough transform.

In step S504, the contour extracting unit 122 confirms whether thecircular contour 514 extracted in step S503 is valid as a contour of theirradiation field and the collimator region. For example, the contourextracting unit 122 can determine whether the position of centercoordinates of the extracted circle (or ellipse) and its radius (ormajor axis and minor axis) are included in a predetermined range. Forexample, the contour extracting unit 122 extracts, as the contour, acircle that is extracted as a contour candidate and for which theposition of center coordinates and the radius are included in apredetermined range. Alternatively, the contour extracting unit 122extracts, as the contour, an ellipse that is extracted as a contourcandidate and for which the position of center coordinates and the majoraxis and the minor axis are included in a predetermined range.

Alternatively, the contour extracting unit 122 can determine whether aregion that is formed by the extracted circle (or ellipse) overlaps andmatches well with the region 515 that is highly likely to be theirradiation field in the probability map 512, for example, whether anoverlap ratio that indicates the ratio of overlap between these regionsis at least a reference value. If the overlap ratio that indicates theratio of overlap between a region based on the contour candidate (regionbased on the extracted circular contour 514) and an irradiation fieldpresumed from the probability map 512 is at least a threshold value, thecontour extracting unit 122 extracts the contour candidate (circularcontour 514) as the contour.

With regard to confirmation of validity of the contour, the contourextracting unit 122 can perform determination processing that matchesfeatures of the image obtained through radiation imaging, such as theimaged portion of the subject.

Through the processing performed in above steps S501 to S504, thecontour extracting unit 122 can extract contours with high accuracy,even when a circular collimator is used for narrowing down theirradiation field.

It should be noted that, if examples of rectangular collimators andexamples of circular collimators (including elliptical collimators) areboth included in the training data used in learning by the inferenceunit 121, the inference unit 121 can obtain the probability map 512regardless of whether the collimator has a rectangular shape or acircular shape. Using these properties, a configuration may be employedin which, according to the shape of the obtained probability map, themost suitable contour extracting unit 122 can be selected from that forrectangular contours described in the first embodiment and that forcircular contours described in the second embodiment.

For example, a configuration may be employed in which a user performsthe selection through the operation panel 114 or a configuration may beemployed in which the contour extracting unit 122 performs bothprocessing for rectangular contours and processing for circular contoursand the field extracting unit 123 automatically extracts, as theirradiation field 221, a region in which the ratio of overlap between aregion formed by extracted contours and a region that is highly likelyto be the irradiation field in the probability map is at least apredetermined reference value.

Third Embodiment: Learning Mechanism at User Site

Next, a third embodiment will be described. FIG. 6 is a block diagramshowing an exemplary basic configuration of a radiation imaging systemthat includes an image processing apparatus of the third embodiment. Theconfiguration of the third embodiment differs from that of the firstembodiment in that, in addition to the same elements as those in thefirst embodiment, a learning apparatus 601 is included in theinformation processing apparatus 107.

In the first and second embodiments, the radiation imaging system 100 isconfigured such that the inference unit 121 only performs inferenceprocessing using a neural network and learning by the neural network isperformed in advance.

In the third embodiment, the radiation imaging system 100 is configuredsuch that images that are obtained in the usage environment of the userand a set of datasets of the irradiation field are accumulated in thestorage apparatus 115. As a result of the learning apparatus 601 beingelectrically connected to the CPU bus 111 in the information processingapparatus 107, learning processing can also be performed in theinformation processing apparatus 107 of the radiation imaging system100. The inference unit 121 performs inference processing based on aresult of learning to which the images obtained in the usage environmentof the user and the set of datasets of the irradiation field are newlyadded and a result of learning performed in advance. Thus, additionallearning processing using the learning apparatus 601 can be performedusing the set of datasets stored in the storage apparatus 115 as newtraining data to update a parameter group in the inference unit 121. Itshould be noted that a calculation unit that has high parallel operationperformance such as GPU can be used as the learning apparatus 601.

Any timing can be selected as the timing for performing the additionallearning such as when at least a predetermined number of datasets thatserve as new training data are accumulated in the storage apparatus 115or at least a predetermined number of datasets that are obtained throughmodification of irradiation field recognition results by the user areaccumulated. Also, transfer learning can be performed by setting aparameter group that was used prior to learning as default values of aparameter group of the neural network when additional learning isperformed by the inference unit 121.

The storage apparatus 115 and the learning apparatus 601 do notnecessarily have to be directly installed in the information processingapparatus 107, and may be provided on a cloud server that is connectedto the information processing apparatus 107 via a network. In this case,datasets that are obtained by a plurality of radiation imaging systems100 can be collected and stored on the cloud server and additionallearning can also be performed using these datasets.

As described above, according to the third embodiment, image processingtechnology can be provided that not only achieves the effects of thefirst and second embodiments but also enables more accurate extractionof an irradiation field by optimizing irradiation field recognitionprocessing so as to match the usage environment of the user.

Although some embodiments of the present invention have been described,it goes without saying that the present invention is not limited tothose embodiments and can be carried out with various alterations andmodifications within the scope of the gist of the present invention.

Other Embodiments

Embodiment(s) of the present invention can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2018-152722, filed Aug. 14, 2018, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An image processing apparatus configured toextract an irradiation field from an image obtained through radiationimaging, comprising: an inference unit configured to obtain anirradiation field candidate in the image based on inference processing;a contour extracting unit configured to extract a contour of theirradiation field based on contour extraction processing performed onthe irradiation field candidate; and a field extracting unit configuredto extract the irradiation field based on the contour.
 2. The imageprocessing apparatus according to claim 1, wherein the inference unitobtains, as the irradiation field candidate, a probability map thatindicates a probability of being an irradiation field or a probabilityof not being an irradiation field for each pixel of the image.
 3. Theimage processing apparatus according to claim 2, wherein the contourextracting unit extracts the contour of the irradiation field from animage that is obtained from the probability map and includes an edgethat indicates a boundary between the irradiation field and a collimatorregion.
 4. The image processing apparatus according to claim 2, whereinthe contour extracting unit performs, on the irradiation fieldcandidate, contour extraction processing for extracting the contourbased on a shape of a collimator.
 5. The image processing apparatusaccording to claim 4, wherein the contour extracting unit changes thecontour extraction processing according to the shape.
 6. The imageprocessing apparatus according to claim 4, wherein the contourextracting unit performs rectangular contour extraction processing ifthe shape is rectangular and performs circular contour extractionprocessing if the shape is circular.
 7. The image processing apparatusaccording to claim 6, wherein the contour extracting unit is capable ofselecting the rectangular contour extraction processing for therectangular shape and the circular contour extraction processing for thecircular shape.
 8. The image processing apparatus according to claim 6,wherein the contour extracting unit extracts a straight line as acontour candidate through the rectangular contour extraction processing.9. The image processing apparatus according to claim 8, wherein thecontour extracting unit extracts, as the contour, a straight line thatis longer than a predetermined length, out of straight lines that areeach extracted as the contour candidate.
 10. The image processingapparatus according to claim 6, wherein the contour extracting unitextracts a circle or an ellipse as a contour candidate through thecircular contour extraction processing.
 11. The image processingapparatus according to claim 10, wherein the contour extracting unitextracts, as the contour, a circle that is extracted as the contourcandidate and for which a position of center coordinates of the circleand a radius of the circle are included in a predetermined range. 12.The image processing apparatus according to claim 10, wherein thecontour extracting unit extracts, as the contour, an ellipse that isextracted as the contour candidate and for which a position of centercoordinates of the ellipse and a major axis and a minor axis of theellipse are included in a predetermined range.
 13. The image processingapparatus according to claim 8, wherein, if an overlap ratio thatindicates a ratio of overlap between a region based on the contourcandidate and an irradiation field presumed from the probability map isat least a threshold value, the contour extracting unit extracts thecontour candidate as the contour.
 14. The image processing apparatusaccording to claim 1, wherein the field extracting unit extracts aregion inside the contour as the irradiation field.
 15. The imageprocessing apparatus according to claim 2, wherein the field extractingunit extracts the irradiation field based on a region in which anirradiation field presumed from the contour overlaps with an irradiationfield presumed from the probability map.
 16. The image processingapparatus according to claim 15, wherein the field extracting unitextracts, as the irradiation field, a region in which a ratio of overlapbetween the irradiation field presumed from the contour and theirradiation field presumed from the probability map is at least a setvalue.
 17. The image processing apparatus according to claim 1, whereinthe inference unit performs the inference processing based on learningperformed using a set of data that includes the image as input and theirradiation field as output.
 18. The image processing apparatusaccording to claim 1, wherein the inference unit performs the inferenceprocessing based on a result of learning that is newly added based onimages obtained in a usage environment of a user and a set of datasetsof the irradiation field, and a result of learning performed in advance.19. The image processing apparatus according to claim 17, wherein theinference unit performs the inference processing based on a result ofthe learning that includes learning by a neural network.
 20. An imageprocessing method for extracting an irradiation field from an imageobtained through radiation imaging, comprising: obtaining an irradiationfield candidate in the image based on inference processing; extracting acontour of the irradiation field based on contour extraction processingperformed on the irradiation field candidate; and extracting theirradiation field based on the contour.
 21. A storage medium in which aprogram for causing a computer to execute each step in the imageprocessing method according to claim 20 is stored.